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» Learning for Evolutionary Design
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ATAL
2005
Springer
14 years 3 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
SIGCSE
2008
ACM
143views Education» more  SIGCSE 2008»
13 years 9 months ago
Test-driven learning in early programming courses
Coercing new programmers to adopt disciplined development practices such as thorough unit testing is a challenging endeavor. Test-driven development (TDD) has been proposed as a s...
David Janzen, Hossein Saiedian
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
14 years 3 months ago
Statistical analysis of heuristics for evolving sorting networks
Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960’s. The application of evolutionary computing to this problem ...
Lee K. Graham, Hassan Masum, Franz Oppacher
GECCO
2003
Springer
182views Optimization» more  GECCO 2003»
14 years 3 months ago
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
Allan Tucker, Xiaohui Liu, David Garway-Heath
GECCO
2009
Springer
161views Optimization» more  GECCO 2009»
14 years 4 months ago
Are evolutionary rule learning algorithms appropriate for malware detection?
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...